Deep learning integral imaging for three-dimensional visualization, object detection, and segmentation
| Autor: | Bahram Javidi, Faliu Yi, Inkyu Moon, Ongee Jeong |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Integral imaging
Computer science business.industry Mechanical Engineering Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 021001 nanoscience & nanotechnology Tracking (particle physics) 01 natural sciences Convolutional neural network Atomic and Molecular Physics and Optics Object detection Electronic Optical and Magnetic Materials Visualization 010309 optics 0103 physical sciences Computer vision Segmentation Artificial intelligence Electrical and Electronic Engineering 0210 nano-technology Focus (optics) business |
| Zdroj: | Optics and Lasers in Engineering. 146:106695 |
| ISSN: | 0143-8166 |
| DOI: | 10.1016/j.optlaseng.2021.106695 |
| Popis: | A depth slice image that is computationally reconstructed from an integral imaging system consists of focused and out of focus areas. The unfocused areas affect three-dimensional (3D) image analyses and visualization including 3D object detection, extraction, and tracking. In this work, we present a deep learning integral imaging system that can reconstruct a 3D image without the out of focus areas and can accomplish target detection and segmentation at the same time. A Mask-Regional Convolutional Neural Network (Mask-RCNN) deep learning algorithm was trained using a public dataset and applied to detect and segment multiple targets in two-dimensional (2D) elemental images in the integral imaging system. The 3D images were then reconstructed using segmented elemental images with the target detected. The proposed method works well in the presence of partial occlusions. Experimental results show the performance of the proposed scheme. |
| Databáze: | OpenAIRE |
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